The Image Processing module is designed to run and consult image processing
jobs and artifacts in the WIPP system. It is accessible from the "Image Processing"
tab from the top menu bar, and allows:
- image calibration (flat field and background correction)
- image tile stitching
- image segmentation to obtain a binary mask or labeled image
- image intensity scaling
- object tracking
- image pyramid building for deep zoom viewing
- configuration of multiple image pyramids into a multiple layer deep zoom visualization
- image tessellation to create rectangular or hexagonal image partitions
- image assembly of tiles
- object labeling from binary masks
WIPP Image Processing module screenshot
Click on the one of the
tabs on the left menu to access the corresponding list of jobs or artifacts.
Below are explanations on how to configure and run image processing
jobs per category.
For running a job using an Image collection, you must wait
for images to be uploaded and converted to ome.tif before launching.
Stitching jobs
This processing step generates position information about each small
field of view image in the coordinate system of a large field of view image.
From the Image Processing view, click on the "Stitching jobs" tab
to access the Stitching jobs view. The list of jobs can be sorted by name, status,
creation date, start time and end time, and filtered by name or status.
WIPP Stitching jobs screenshot
To create a new Stitching job, click on the "Create new job" button.
Below is a description on how to configure each input parameter.
WIPP Stitching job with MIST screenshotWIPP Stitching job with metadata screenshotWIPP Stitching job Mosaic screenshotWIPP Stitching job Time series of 1 FOV screenshot
Job name
Unique name for the job. The resulting stitching vector will be named
after the stitching job name.
Grid collection
Input image collection.
Algorithm
MIST
A stitching algorithm for small and large two-dimensional (2D) image grid
collections. This method is used for optimizing the translations computed
by the phase-correlation method using the Fourier transform approach.
Learn more by clicking
here.
Stage metadata
This option uses the microscope stage information recorded during the acquisition
of each small image tile (one field of view) to compute its global position in the
grid of image tiles (one large field of view).
No Overlap Mosaic
This option creates a stitching vector for image tiles without any spatial
overlap using the MIST algorithm.
Time Sequence of 1 FOV
This option is useful when building a pyramid from a sequence of images.
It does not perform any stitching because the input collection has only one
field view per time point. The images are sorted alphabetically if no filename pattern
is inputed, or sequentially according to a temporal filename pattern.
File name pattern type
This field is used to specify the type of filename pattern used in the
acquired images.
Possible options: Sequential and Row-Column.
Sequential Filename Pattern Types
Sequential Filename Pattern Types have only one set of curly brackets "{}".
The type denotes the image position in the image grid. The special character
"p" must be used between the curly brackets. Therefore, a valid sequential
filename pattern must have one set of curly brackets "{p}" with at least
one "p" character between the curly brackets. Examples:
1. Img_pos001.ome.tif = Img_pos{ppp}.ome.tif
2. ImageName01.ome.tif = ImageName{pp}.ome.tif
Row-column Filename Pattern Types
Row-column Filename Pattern Types have two sets of curly brackets "{r}" "{c}".
One block denotes the image row index within the image grid and uses the
special character "r". The other block denotes the image column index
within the image grid and uses the special character "c".
For a valid row-column Filename Pattern there must be one "{r}" block
with at least one "r" between the curly brackets and one "{c}" block with
at least one "c" between the curly brackets. Examples:
The usage of "{p}" or "{r}" and "{c}" must match the Filename Pattern Type selected.
If the Filename Pattern contains "{p}" the sequential Filename Pattern Type
must be selected. If the Filename Pattern contains "{r}" and "{c}" then row-column
Filename Pattern Type must be selected.
Filename pattern
The Filename Pattern is used to match specific image files within the Image
Collection. The stage metadata algorithm does not support this field.
MIST and No Overlap Mosaic
There are two types of Filename Pattern, Sequential and
Row-Column (see Filename Pattern Types above), both of which can handle time slices.
Note: Time Slices
MIST algorithm can stitch a series of independent 2D image grids,
for example, a time-lapse series of image grids. The time-slice stitching
is controlled by an additional set of curly brackets in the Filename
Pattern with the "{ttt}" special text. The special text "{ttt}" must
be used regardless of whether the independent 2D image grid are time
slices or z-stack slices.
Examples:
Only the time index as in "{ttt}" is accepted when using this algorithm.
Since this option does not stitch the images of he input collection, grid indexes used in MIST such as
"{rrr}", "{ccc} or "{ppp}" are not handled, and therefore a warning message is displayed when they are
used in the filename pattern.
Examples:
1. Img_time01.ome.tif = Img_time{tt}.ome.tif
2. Img_t001.ome.tif = Img_t{ttt}.ome.tif
The file name pattern can also be left blank. In this case, the images will be sorted
alphabetically and time slices are created according to that order.
Time slices
Leave this field blank to stitch all time slices (Starting from 0 or 1).
To stitch time slices, you must add the special format text "{ttt}" to the
Filename Pattern. If there is no special format text "{ttt}" in the Filename Pattern
then this field must be blank. This input supports a single value
or a range using a '-'. Examples:
1. "1-25" stitches timeslices 1 through 25
(Note: pyramid building does not support time slices that are not contiguous)
2. "" stitches all available timeslices
3. "3" stitches timeslice 3
4. "0" stitches timeslice 0
Starting point
The starting point of the microscope scan. Possible options: Top Left,
Top Right, Bottom Left, Bottom Right. This specifies the scanning origin for
the input collection (a grid of images).
Direction
The direction and pattern of the microscope motion during acquisition.
Possible options: Vertical Combing, Vertical Continuous, Horizontal
Combing, and Horizontal Continuous.
Stage repeatability
Sets the stage repeatability variable when computing the global optimization.
This value is used to represent the repeatability of the microscope stage movement
(A to B and then back to A ~ delta A).
It is used for determining the search space of the hill climbing algorithm.
This value is specified in pixels.
Number of columns
The number of images in one row of the grid (The number of columns,
the width of the image grid).
Number of rows
The number of images in one column of the grid (The number of rows,
the height of the image grid).
Horizontal overlap
Sets the horizontal spatial overlap of adjacent image tiles. The value
is used when optimizing the global position of image tiles to filter
translations as good or bad. Good translations serve as starting positions
for the hill climbing algorithm. Setting this value can improve the accuracy
of stitching. This value is specified in percent and must be between 0 and 100.
Vertical overlap
Sets the vertical spatial overlap of adjacent image tiles. The value
is used the same way as the horizontal overlap. This value is specified
in percent and must be between 0 and 100.
Filtering jobs
Microscopy imaging introduces a variety of artifacts including noise.
This processing step is designed to reduce the effect of noise on image quality.
From the Image Processing view, click on the "Filtering jobs" tab
to access the Filtering jobs view. The list of jobs can be sorted by name, status,
creation date, start time and end time, and filtered by name or status.
WIPP Filtering jobs screenshot
To create a new Filtering job, click on the "Create new job" button.
Below is a description on how to configure each input parameter.
WIPP Filtering job (mean filter) screenshot
Job name
Unique name for the job. The resulting image collection will be named
after the filtering job name.
Image collection
Input image collection.
Filter type
Choose a filter type among the available options: Mean, Median, Min, Max and Gaussian blur.
Radius
Kernel size in pixels.
Flat Field Correction jobs
This processing step corrects the distortions introduced by the optics in microscopes.
From the Image Processing view, click on the "Flat Field Correction jobs" tab
to access the Flat Field Correction jobs view. The list of jobs can be sorted by name, status,
creation date, start time and end time, and filtered by name or status.
WIPP Flat Field Correction jobs screenshot
To create a new Flat Field Correction job, click on the "Create new job" button.
Below is a description on how to configure each input parameter.
WIPP Flat Field Correction job screenshot
Job name
Unique name for the job. The resulting image collection will be named
after the job name.
Image collection
Input image collection to be corrected.
Dark collection
Image collection containing one image acquired by the microscope with a closed camera shutter.
Fluorescein collection
Image collection containing one image of a solution without cells acquired by the microscope.
Background Correction jobs
This processing step corrects intensity values in fluorescent microscopy
images by subtracting the fluorescent signal introduced by the surrounding
media from the signal measured at the cell locations.
From the Image Processing view, click on the "Background Correction jobs" tab
to access the Background Correction jobs view. The list of jobs can be sorted by name, status,
creation date, start time and end time, and filtered by name or status.
WIPP Background correction jobs screenshot
To create a new Background Correction job, click on the "Create new job" button.
Below is a description on how to configure each input parameter.
WIPP Background Correction job screenshot
Job name
Unique name for the job. The resulting image collection will be named
after the job name.
Image collection
Input image collection to be corrected.
Binary collection
Image collection of binary images. Binary values are 0 for background and 1 for foreground.
Image file names in the binary image collection should match those from the input image collection.
Gap size
Distance in pixels between the circumference of the region of interest to the closest
inner circumference of the doughnut area to be considered as background, in pixels.
Ring size
Thickness of the doughnut area or the distance in pixels between the inner and outer rings.
This step builds a large field of view image from many small field of view images.
The resulting assembled images can then be used, for instance, to compute features
using the Web Feature Extraction.
From the Image Processing view, click on the "Image Assembling jobs" tab
to access the Image Assembling jobs view. The list of jobs can be sorted by name, status,
creation date, start time and end time, and filtered by name or status.
WIPP Image Assembling jobs screenshot
To create a new Image Assembling job, click on the "Create new job" button.
Below is a description on how to configure each input parameter.
WIPP Image Assembling job screenshot
Job name
Unique name for the job. The resulting image collection will be named
after the job name.
Stitching vector
Metadata of the location of each image in the acquired grid, after stitching is performed.
Grid collection
Collection of images as acquired on the microscope as a grid.
Intensity Scaling jobs
Images with the bit depth more than 8 bits per pixel (BPP) cannot be rendered
in current web browsers. The intensity scaling job distributes the intensity values
so that the 8 BPP image rendering delivers sufficient contrast for visual inspection.
Since microscopy images are acquired with the bit depth larger than 8 BPP and the
intensities might not be evenly distributed, this processing step can be executed
before pyramid building.
From the Image Processing view, click on the "Intensity Scaling jobs" tab
to access the Intensity Scaling jobs view. The list of jobs can be sorted by name, status,
creation date, start time and end time, and filtered by name or status.
WIPP Intensity Scaling jobs screenshot
To create a new Intensity Scaling job, click on the "Create new job" button.
Below is a description on how to configure each input parameter.
This computational job will create a new image collection to store the intensity rescaled results.
WIPP Intensity Scaling job screenshot
Job name
Unique name for the job. The resulting image collection will be named
after the job name.
Image collection
Input image collection.
Intensity scaler type
Use truncation unless there is a compelling reason not to.
There is two types of intensity scaling:
Truncation (preferred)
By default, it will saturate the bottom and top 1% of intensities, linearly
mapping intensities into that range. This is the same as Fiji/ImageJ auto-contrast.
If range start and end are specified those values are used instead of
computing range start as 1st percentile and end as 99th percentile.
Gamma Correction
This performs a non-linear (exponential) rescaling. By default the start
and end values are the 1st percentile and the 99th percentile.
EGT Segmentation jobs
This processing step labels image pixels as background and foreground.
From the Image Processing view, click on the "EGT Segmentation jobs" tab
to access the EGT Segmentation jobs view. The list of jobs can be sorted by name, status,
creation date, start time and end time, and filtered by name or status.
WIPP EGT Segmentation jobs screenshot
To create a new EGT Segmentation job, click on the "Create new job" button.
Below is a description on how to configure each input parameter.
WIPP EGT Segmentation job screenshot
Job name
Unique name for the job. The resulting image collection will be named
after the job name.
Image collection
Input image collection to segment.
Min object size
The size above which a detected foreground is considered as a region of interest.
All objects below this value are considered as noise and are eliminated from the segmented mask.
Keep Holes with
Minimum hole size [pixels]
Minimum size of holes below which a hole will be filled.
Maximum hole size [pixels]
Maximum size of holes above which a hole will be filled.
Join operator
Boolean operation of “AND” or “OR”.
Minimum intensity of a hole [percentile]
The minimum average intensity for a hole below which it will be filled.
Maximum Intensity of a hole [percentile]
The maximum average intensity for a hole below which it will be filled.
Greedy
Controls how greedy foreground is with respect to background. If the segmentation
is mislabeling some foreground pixels as background pixels then increasing the greedy
parameter in the positive direction results in labeling more image pixels as foreground pixels.
This processing step labels image pixels with a unique label and has been
specifically designed for cell segmentation from phase contrast images
(including mitotic events).
From the Image Processing view, click on the "Fogbank Segmentation jobs" tab
to access the Fogbank Segmentation jobs view. The list of jobs can be sorted by name, status,
creation date, start time and end time, and filtered by name or status.
WIPP Fogbank Segmentation jobs screenshot
To create a new Fogbank Segmentation job, click on the "Create new job" button.
Below is a description on how to configure each input parameter.
WIPP Fogbank Segmentation job screenshot
Job name
Unique name for the job. The resulting image collection will be named
after the job name.
Image collection
Input image collection to segment.
Labeled collection
Binary image collection which contains the background and foreground labels.
Use Border Mask
The geodesic distance dI (a,b) between two pixels a and b in the image I,
is the minimum of the length L of the path(s) P = (c1,c2, ..., cl) joining p and q in I.
Fogbank geodesic distance
dI (a,b) = ∞, if a and b are not connected in I. The geodesic distance prevents
pixels that are close to a cell but separated by a border from being assigned to that cell.
Those pixels are instead assigned to a different cell that is further away in terms of
number of pixels on the image, but closer in terms of geodesic distance
as shown in the following picture.
A schematic figure to display the allocation of an unassigned
pixel (x marked) to the closest seed point (yellow path) by means of the minimum
geodesic distance between that pixel and the seed points in the image.
The yellow path has a geodesic distance smaller than the orange or green path.
The red pixels represent cell boundaries that cannot be traversed.
There are two choices to define the border mask: (1) all pixels can be traversed,
or (2) the geodesic mask is used. The geodesic mask is a binary image where
pixels with value equal to zero represent boundaries that cannot be traversed,
and pixels with value equal to one are paths that can connect two pixels of
interest together. Borders are defined through the input Percentile Threshold.
This mask can help separate single cells with boundaries close to manually drawn ones.
Geodesic mask
Minimum Seed Size
The detection of seed points determines whether an image is over or under-segmented.
There are three different methods for automatic detection of seed points that minimize
over-segmentation: (1) Apply Minimum Seed Size Threshold on every histogram percentile
binning quantization that filters the small noisy seed points. (2) Generate a fixed number
of seed points per frame, which incorporates biological insight to locate the seeds.
(3) Import the seed mask. The choice depends on the problem being solved.
This method computes seed points as a function of histogram percentile binning quantization with
seed size constraint. In contrast with other techniques, intensity thresholds are not
defined at every unique intensity value in the image but rather on each percentile value
of the image. Using every unique value leads to multiple local peaks and thus to over-segmentation,
but binning the pixel intensities reduces the over-segmentation.
Minimum Object Size
This parameter represents the minimum size that any cellular object must have in order to
be recognized as a single cell. Any object with the size smaller than this threshold will be
deleted from the mask and its corresponding pixels will be assigned to the closest
neighboring cell.
Fogbank Direction
This selects the direction between seed points and boundaries. If low intensity
pixels correspond to seed points and high intensity pixels correspond to boundaries,
then Fogbank direction should be from Min to Max and vice-versa
Foreground masks can be generated by running the EGT segmentation job on the
image collection to segment.
For more details, click
here.
CNN Segmentation jobs (beta)
This processing step labels image pixels as background and foreground using Convolutional
Neural Networks (CNN) in TensorFlow.
From the Image Processing view, click on the "CNN Segmentation jobs" tab
to access the CNN Segmentation jobs view. The list of jobs can be sorted by name, status,
creation date, start time and end time, and filtered by name or status.
WIPP CNN Segmentation jobs screenshot
To create a new CNN Segmentation job, click on the "Create new job" button.
Below is a description on how to configure each input parameter.
WIPP CNN Segmentation job screenshotWIPP CNN Trained Params screenshot
Job name
Unique name for the job. The resulting image collection will be named
after the job name.
Image collection
Input image collection to segment.
Trained Model
Chose the pre-trained network model parameters to load.
The trained parameters details are available from the CNN Trained Parameters tab on the left menu.
Two sets of trained parameters are currently available in WIPP, using a U-net model.
A schematic of the architecture of the U-Net model
The architecture has four convolutional layers and four deconvolutional layers. The depth of the first convolutional layer is fixed at 64 and the following layers double in depth with each layer. The size of the convolutional kernels is fixed at 5x5 with a stride of 2. The model was implemented in Tensorflow 1.2 and is currently running on Python2.7. The implementation is based on the original paper:
Ronneberger, O., Fischer, P., & Brox, T. (2015, October). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 234-241).
Tracking jobs
The Lineage-Mapper tracks a series of labeled segmented images.
A labeled image is a segmented image where the regions of interest (ROI) are labeled
from 1 to maximum number of objects per image. The ROI numbering does not need to
reflect any organization. The labeled ROIs in the segmented images all consist of
pixels that have the value of the ROI label. For example, every pixel in the ROI
labeled 5 has a pixel value of 5. Background pixels have the value 0.
For more details, click
here.
From the Image Processing view, click on the "Tracking jobs" tab
to access the Tracking jobs view. The list of jobs can be sorted by name, status,
creation date, start time and end time, and filtered by name or status.
WIPP Tracking jobs screenshot
To create a new Tracking job, click on the "Create new job" button.
Below is a description on how to configure each input parameter.
WIPP Tracking job screenshot
Job name
Unique name for the job. The resulting image collection will be named
after the job name.
Image collection
Input image collection to track.
File name pattern
Used to select which images to load from a directory. Important: the
character "%" cannot be used as part of a filename pattern.
Example: img_001.tif = img_{iii}.ome.tif
{iii} - Special text that represents index numbering between 0 and 999.
Increase the number of i's to represent larger numbers.
Prefix
The prefix given to output files.
Minimum Object Size
The minimum size a labeled foreground object must be in order to be
recognized as a cell. Objects smaller than this threshold will be deleted
if they were created by splitting the group of cellular area into single cell segments.
This parameter is only used when a fusion cases is detected and the cells are cut apart.
Otherwise no minimum object size will be enforced on the input labeled masks.
Maximum Centroid Displacement
The maximum centroid distance (in pixels) is used to consider which cells could
possibly be tracked together. The radius from a cell centroid to the max centroid
distance represents the area of a possible cell migration. For example, the red cell
represents the current frame, whereas the blue cells represent the next frame.
The Cell Tracker would consider the upper blue cell as a possible tracking option
to the red cell and the lower blue cell would be ignored.
WIPP Tracking Maximum Centroid Displacement
Enable Cell Division
If cell division is enabled, then the daughters cells of a mitotic event will
be assigned new cell labels that are different from their mother cell label.
If disabled, then the daughters will keep the same label as the mother cell
and no mitotic event is considered. This functionality is helpful
when dealing with particle tracking or colony tracking.
The cell tracker bases its decision on detecting mitotic events using
cell overlap between the mother and its two daughter cells. If the cell
overlap between the current frame and the next frame is above the Min Division
Overlap threshold, then the Cell Tracker labels that as a possible
mitotic event. The Cell Tracker then tests the Daughter Size Similarity,
Daughter Aspect Ratio Similarity, and Mother Circularity Index thresholds
to determine if a mitotic event has occurred. If all of tests pass, then the
Cell Tracker records the mitotic event in the division table.
Minimum Division Overlap
If the cell overlap in percent is above this threshold between the current frame and
the next frame, then the Cell Tracker records a possible mitotic event.
The following table illustrates the value of this parameter with respect to the overlapping
positions between a red cell from the current frame and the blue cell from the next frame.
If this parameter
is set to 0%, then all cases are considered as potential mitotic events.
If this parameter is set to 100%, then cell mitosis is discarded. In this case, the daughter
cell that overlaps the most with the mother cell keeps its unique global
ID label and the other one is assigned a new label.
WIPP Tracking Minimum Division Overlap
Daughter Size Similarity
This parameter is a measure of the size similarity between daughter cells.
In a real mitotic event, the sizes of the daughter cells should be very
similar to each other. A mother cell does not really produce a large
daughter and a small one. Set this parameter to 0% to discard it.
WIPP Tracking Daughter Size Similarity
Daughter Aspect Ratio Similarity
This parameter is a measure of the aspect ratio similarity between daughter
cells. In a real mitotic event daughter cells should have similar shapes
to each other. Set this parameter to 0% to discard it.
WIPP Tracking Daughter Aspect Ratio Similarity
Mother Circularity Threshold
For a cell to be considered a mother cell in a possible mitotic event it
must have had a round shape during the previous Number of Frames to Check Circularity
parameter. This circularity threshold determines what is round enough to be
considered a mitotic cell. Set this parameter to 0% to discard it.
WIPP Tracking Mother Circularity Threshold
Number Frames to Check Circularity
The Cell Tracker will determine if the cell had a circularity threshold
above the Mother Circularity Index between the current frame and the
previous number of frames. If the cell’s circularity is not above the
threshold at least for one frame within this range, then the mitotic
event will not be recorded.
Enable Fusion
If cell fusion is enabled, the cell tracker will assign a new unique global ID
number to the fused region and will consider all the cells from the previous frame
as dead. If disabled, the cell tracker will separate the cellular area in the
current frame into a group of single cells by relying on the previous frames information.
Cell fusion occurs when multiple cells get together and form one cellular object.
It can come from an actual fusion where, for example, two colonies merge into one or
from cells migrating so close together that segmentation technique considers them a single cell.
Minimum Fusion Overlap
This parameter represents the amount of overlap in percent of cell area, above
which an area at the current frame is considered as a group of cells from the
previous frame. In this case, this area needs to be split into multiple single cells.
For example: if two cells A and B at frame t have tracks to the same cell C at frame t+1
and the amount of overlap between A and C = 45% of size A and the overlap between
B and C is 50% of size B, then C should be split into two single cells.
Mask Labeling jobs
This step is used for assigning unique labels to contiguous sets of
pixels in binary images (such as the ones generated
by EGT segmentation or by thresholding). The labeled images (masks)
can be used by the Web Feature Extraction for computing features.
From the Image Processing view, click on the "Mask Labeling jobs" tab
to access the Mask Labeling jobs view. The list of jobs can be sorted by name, status,
creation date, start time and end time, and filtered by name or status.
WIPP Mask Labeling jobs screenshot
To create a new Mask Labeling job, click on the "Create new job" button.
Below is a description on how to configure each input parameter.
WIPP Mask Labeling job screenshot
Job name
Unique name for the job. The resulting image collection will be named
after the job name.
Binary collection
Input image collection to label.
Connectivity
Specified as the values 4, for 4-connected objects, or 8, for 8-connected objects (recommended).
Pyramid Building jobs
In order to visually inspect very large images using the Deep Zoom viewer, microscopy image tiles
need to be converted into pyramids that can be viewed in the browser.
From the Image Processing view, click on the "Pyramid Building jobs" tab
to access the Pyramid Building jobs view. The list of jobs can be sorted by name, status,
creation date, start time and end time, and filtered by name or status.
WIPP Pyramid jobs screenshot
To create a new Pyramid Building job, click on the "Create new job" button.
Below is a description on how to configure each input parameter.
WIPP Pyramid job screenshot
Job name
Unique name for the job. The resulting pyramid will be named
after the job name.
Stitching vector
Input image collection to label.
Image collection
Input image collection to label.
If images names in the image collection and the stitching vector differ,
you must specify the images pattern. The system will try to guess the patterns
automatically but you should double check.
Scale Input Images
Recommended if the images from the input collection are not 8bpp. Checking
this option will scale the images before creating the pyramid, using a truncation
1-99 percentile scaling.
Deep Zoom Visualizations
Multiple pyramids can be combined into a single visualization with multiple layers.
This step must be executed once all pyramids have been built.
From the Image Processing view, click on the "Visualizations" tab
to access the Visualizations view. The list of jobs can be sorted by name, status,
creation date, start time and end time, and filtered by name or status.
WIPP Visualizations screenshot
To create a new Visualization, click on the "Create a new visualization" button.
Below is a description on how to configure each input parameter.
WIPP Visualization screenshot
Visualization name
Unique name for the visualization
Groups
Create one or more groups to group pyramids.
Give the group a label and click "+"
Layers
Add layers (pyramids) to the group with "+". For example, add Transmitted, Excitation, Segmentation (if pyramid was built from the masks)
Tessellation jobs
This processing step is executed to
generate a mask that sub-divides any image into rectangular or
hexagonal partitions (tesselations). Tessellation masks are used
together with segmentation masks to compute spatially local image features for
studying spatial heterogeneity. This step is independent of all other steps.
From the Image Processing view, click on the "Tessellation jobs" tab
to access the Tessellation jobs view. The list of jobs can be sorted by name, status,
creation date, start time and end time, and filtered by name or status.
WIPP Tessellation jobs screenshot
To create a new Tessellation job, click on the "Create new job" button.
Below is a description on how to configure each input parameter.
WIPP Tessellation job for a collection screenshotWIPP Tessellation job for one image screenshot
Job name
Unique name for the job, the resulting image collection will be named after the job name.
Tile shape
Specify the shape of each partition in a tessellation image (square or hexagon).
Radius
Specify the radius of the shape in a tessellation:
- For square shapes, the radius is one half of its width.
- For hexagonal shapes, the radius is the distance between the center and any of the summits.
Option: Generate for an image collection
Choose this option to generate one tessellation image for each image of an image collection.
Mask width and height will be automatically determined from the image.
Image collection
Specify the input image collection.
Option: Generate a single mask
Choose this option to generate a single tessellation image with fixed width and height.
A visualization PNG image will also be created.
Mask width
Specify the mask width of the final tessellation image.
Mask height
Specify the mask height of the final tessellation image.
Image Type Conversion jobs
This step allows the conversion of the image type of images from an
input image collection to a target image type, 8 bpp, 16 bpp or 32 bpp.
From the Image Processing view, click on the "Image Type Conversion jobs" tab
to access the Image Type Conversion jobs view. The list of jobs can be sorted by name, status,
creation date, start time and end time, and filtered by name or status.
WIPP Type Conversion jobs screenshot
To create a new Image Type Conversion job, click on the "Create new job" button.
Below is a description on how to configure each input parameter.
WIPP Image Type Conversion job screenshot
Job name
Unique name for the job. The resulting image collection will be named
after the job name.
Image collection
Collection of images to be converted.
Convert to
Target image type, 8 bpp, 16 bpp or 32 bpp.
Pyramids and visualizations
For an in-depth user manual of the deep zoom view of pyramids, see
this webpage
(please note that some of deep zoom tools are not available in WIPP, such as colony searching and features).
Example Workflow
The figure below illustrates possible workflows of computational steps that have to
be executed in order to visually inspect two image collections of overlapping fields of views (FOV).
The web image processing always includes stitching, pyramid building, and visualization creation.
If the images have pixel depth more than 8 bits per pixel (BPP), then intensity rescaling has to be executed.
Flat field correction and segmentation steps are optional. However, they are important for quantitative analyses.
Example of a workflow using the Web Image Processing
Pipeline to process two image collections (transmitted and excitation channels)
acquired at the same time (the channels are registered).